1,721,052 research outputs found
The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer
No full textDeep Learning and Medical Image Analysis: Epistemology and Ethical Issues
No full textMachine and deep learning methods applied to medicine seem to be a promising way to improve the perfor-mance in solving many issues from the diagnosis of a disease to the prediction of personalized therapies byanalyzing many and diverse types of data. However, developing an algorithm with the aim of applying it inclinical practice is a complex task which should take into account the context in which the software is devel-oped and should be used. In the first report of the World Health Organization (WHO) about the ethics andgovernance of Artificial Intelligence (AI) for health published in 2021, it has been stated that AI may improvehealthcare and medicine all over the world only if ethics and human rights are a main part of its development.Involving ethics in technology development means to take into account several issues that should be discussedalso inside the scientific community: the epistemological changes, population stratification issues, the opacityof deep learning algorithms, data complexity and accessibility, health processes and so on. In this work, someof the mentioned issues will be discussed in order to open a discussion on whether and how it is possible to address them
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
No full textOver the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data
EXPLAINING THE BEHAVIOUR OF A CONVOLUTIONAL NEURAL NETWORK FOR BREAST DENSITY ASSESSMENT
No full textExplainability Applied to a Deep-Learning Based Algorithm for Lung Nodule Segmentation
No full textDeep learning and computer-aided detection (CAD) methods play a pivotal role in the early detection and diagnosis of various cancer types. The significance of AI in the medical field has become particularly pronounced during the coronavirus pandemic. This study aims to develop a deep learning-based system for segmenting and detecting nodules in the lung parenchyma, utilizing the Luna-16 challenge dataset. The algorithm is divided into two phases: the first phase involves lung segmentation using the previously developed LungQuant algorithm to identify the region of interest (ROI), and the second phase employs a specifically designed and fine-tuned Attention Res-UNet for nodule segmentation. Additionally, the explainable AI (XAI) technique, Grad-CAM, was used to demonstrate the reliability of the proposed algorithm for clinical application. In the initial phase, the LungQuant algorithm achieved an average Dice Similarity Coefficient (DSC) of 90%. For nodule segmentation, the DSC scores were 81% test sets. The model also achieved average sensitivity and specificity metrics of 0.86 and 0.92
Neuroimaging-based methods for autism identification: a possible translational application?
Full text linkClassification methods based on machine Learning (ML) techniques are becoming widespread analysis tools in neuroimaging studies. They have the potential to enhance the diagnostic power of brain data, by assigning a predictive index, either of pathology or of treatment response, to the single subject’s acquisition. ML techniques are currently finding numerous applications in psychiatric illness, in addition to the widely studied neurodegenerative diseases. In this review we give a comprehensive account of the use of classification techniques applied to structural magnetic resonance images in autism spectrum disorders (ASDs). Understanding of these highly heterogeneous neurodevelopmental diseases could
greatly benefit from additional descriptors of pathology and predictive indices extracted directly from brain data. A perspective is also provided on the future developments necessary to translate ML methods from the field of ASD research into the clinic
Multi-Site MRI Data Harmonization with an Adversarial Learning Approach: Implementation to the Study of Brain Connectivity in Autism Spectrum Disorders
Full text linkMagnetic resonance imaging (MRI) nowadays plays an important role in the identification of brain underpinnings in a wide range of neuropsychiatric disorders, including Autism Spectrum Disorders (ASD). Characterizing the hallmarks in these pathologies is not a straightforward task and machine learning (ML) is certainly one of the most promising tools for addressing complex and non-linear problems. ML algorithms and, in particular, deep neural networks (DNNs), need large datasets in order to be properly trained and thus ensure generalization capabilities on new data. Large datasets can be obtained by collecting images from different centers, thus bringing unavoidable biases in the analysis due to differences in hardware and scanning protocols between different centers. In this work, we dealt with the issue of multicenter MRI data harmonization by comparing two different approaches: the analytical ComBat-GAM procedure, whose effectiveness is already documented in the literature, and an originally developed site-adversarial deep neural network (ad-DNN). The latter aims to perform a classification task while simultaneously searching for site-relevant patterns in order to make predictions free from site-related biases. As a case study, we implemented DNN and ad-DNN classifiers to distinguish subjects with ASD with respect to typical developing controls based on functional connectivity measures derived from data of the multicenter ABIDE collection. The classification performance of the proposed ad-DNN, measured in terms of the area under the ROC curve (AUC), achieved the value of AUC = 0.70±0.03, which is comparable to that obtained by a DNN on data harmonized according to the analytical procedure (AUC = 0.71±0.01). The relevant functional connectivity alterations identified by both procedures showed an agreement between each other and with the patterns of neuroanatomical alterations previously detected in the same cohort of subjects
Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset
Full text linkThe investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for the development of effective treatments. This study compares traditional ML and DL classifiers in the analysis of tabular data, in particular, functional connectivity measures obtained from the time series of a public multicenter dataset, and evaluates whether the features that contribute most to the classification task vary depending on the classifier used. Specifically, Support Vector Machine (SVM) classifiers, with both linear and radial basis function (RBF) kernels, and Extreme Gradient Boosting (XGBoost) classifiers are compared against the TabNet classifier (a DL architecture customized for tabular data analysis) and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. Among the latter, SVMs outperform the other classifiers with an AUC of around 75%, whereas the best performances of TabNet and MLP reach 65% and 71% at most, respectively. Furthermore, the analysis of the feature importance showed that the brain regions that contribute the most to the classification task are those primarily responsible for sensory and spatial perception, as well as attention modulation, which is known to be altered in ASDs
One-class support vector machines identify the language and default mode regions as common patterns of structural alterations in young children with autism spectrum disorders
Full text linkThe identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22-72 months of age; mean = 49 months] and non-verbal intelligence quotient (NVIQ) [range: 31-123; mean = 73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ = 70, leading to AUC = 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network-posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus-contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype
Evaluation of 3D radio-frequency electromagnetic fields for any matching and coupling conditions by the use of basis functions
No full textA procedure for evaluating radio-frequency electromagnetic fields in anatomical human models for any matching and coupling conditions is introduced. The procedure resorts to the extraction of basis functions: such basis functions, which represent the fields produced by each individual port without any residual coupling, are derived through an algebraic procedure which uses the S parameter matrix and the fields calculated in one (only) full-wave simulation. The basis functions are then used as building-blocks for calculating the fields for any other S parameter matrix. The proposed approach can be used both for volume coil driven in quadrature and for parallel transmission configuration
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